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Background: Chronic kidney disease (CKD) is a complex disorder that has become a high prevalence global health problem, with diabetes being its predominant pathophysiologic driver. Autosomal genetic variation only explains some of the predisposition to kidney disease. Variations in the mitochondrial genome (mtDNA) and nuclear-encoded mitochondrial genes (NEMG) are implicated in susceptibility to kidney disease and CKD progression, but they have not been thoroughly explored. Our aim was to investigate the association of variation in both mtDNA and NEMG with CKD (and related traits), with a particular focus on diabetes. Methods: We used the UK Biobank (UKB) and UK-ROI, an independent collection of individuals with type 1 diabetes mellitus (T1DM) patients. Results: Fourteen mitochondrial variants were associated with estimated glomerular filtration rate (eGFR) in UKB. Mitochondrial variants and haplogroups U, H and J were associated with eGFR and serum variables. Mitochondrial haplogroup H was associated with all the serum variables regardless of the presence of diabetes. Mitochondrial haplogroup X was associated with end-stage kidney disease (ESKD) in UKB. We confirmed the influence of several known NEMG on kidney disease and function and found novel associations for SLC39A13, CFL1, ACP2 or ATP5G1 with serum variables and kidney damage, and for SLC4A1, NUP210 and MYH14 with ESKD. The G allele of TBC1D32-rs113987180 was associated with higher risk of ESKD in patients with diabetes (OR:9.879; CI95%:4.440–21.980; P = 2.0E-08). In UK-ROI, AGXT2-rs71615838 and SURF1-rs183853102 were associated with diabetic nephropathies, and TFB1M-rs869120 with eGFR. Conclusions: We identified novel variants both in mtDNA and NEMG which may explain some of the missing heritability for CKD and kidney phenotypes. We confirmed the role of MT-ND5 and mitochondrial haplogroup H on renal disease (serum variables), and identified the MT-ND5-rs41535848G variant, along with mitochondrial haplogroup X, associated with higher risk of ESKD. Despite most of the associations were independent of diabetes, we also showed potential roles for NEMG in T1DM. [ABSTRACT FROM AUTHOR]

Genetic effects can be sex-specific, particularly for traits such as testosterone, a sex hormone. While sex-stratified analysis provides easily interpretable sex-specific effect size estimates, the presence of sex-differences in SNP effect implies a SNP×sex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP's main and SNP×sex interaction effects simultaneously. Notably, even without individual-level data, the joint test statistic can be derived from sex-stratified summary statistics through an omnibus meta-analysis. Utilizing the available sex-stratified summary statistics of the UK Biobank, we performed such omnibus meta-analyses for 290 quantitative traits. Results revealed that this approach is robust to genetic effect heterogeneity and can outperform the traditional sex-stratified or sex-combined main effect-only tests. Therefore, we advocate using the omnibus meta-analysis that captures both the main and interaction effects. Subsequent sex-stratified analysis should be conducted for sex-specific effect size estimation and interpretation. Author summary: When genetic variant effects on complex traits differ between females and males, sex-stratified analysis is often applied, offering easy-to-interpret, sex-specific effect estimates. However, from the viewpoint of maximizing the power and robustness of association testing, sex-stratified analysis may not be the best analytical strategy. As sex-specific genetic effects imply an SNP×sex interaction effect, jointly testing SNP main effects and SNP×sex interactions could be more powerful than sex-stratified analysis or the standard main-effect testing. Furthermore, this joint test is applicable even when individual-level data are not available, by leveraging sex-specific summary statistics through an omnibus meta-analysis. In this study, we performed such an omnibus meta-analysis using the UK Biobank data. Across 290 phenotypes, our results showed that this method is generally comparable to traditional analyses and excels by identifying new loci for traits such as testosterone, which standard tests do not detect. Our findings suggest that leveraging genetic heterogeneity enhances the detection of genetic associations, with significant implications for the future analysis of diverse data. [ABSTRACT FROM AUTHOR]

LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h 2 and polygenicity p , so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r 2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses. This study extends the utility of LDpred2-auto, a widely used method for deriving predictive polygenic scores. It validates the use of the latest version of LDpred2-auto for inferring key genetic parameters that inform us about the genetic architecture of traits, for identifying causal genetic variants, and for estimating predictive performance. [ABSTRACT FROM AUTHOR]

Polygenicity refers to the phenomenon that multiple genetic variants have a nonzero effect on a complex trait. It is defined as the proportion of genetic variants with a nonzero effect on the trait. Evaluation of polygenicity can provide valuable insights into the genetic architecture of the trait. Several recent works have attempted to estimate polygenicity at the single nucleotide polymorphism level. However, evaluating polygenicity at the gene level can be biologically more meaningful. We propose the notion of gene‐level polygenicity, defined as the proportion of genes having a nonzero effect on the trait under the framework of a transcriptome‐wide association study. We introduce a Bayesian approach genepoly to estimate this quantity for a trait. The method is based on spike and slab prior and simultaneously estimates the subset of non‐null genes. Our simulation study shows that genepoly efficiently estimates gene‐level polygenicity. The method produces a downward bias for small choices of trait heritability due to a non‐null gene, which diminishes rapidly with an increase in the genome‐wide association study (GWAS) sample size. While identifying the subset of non‐null genes, genepoly offers a high level of specificity and an overall good level of sensitivity—the sensitivity increases as the sample size of the reference panel expression and GWAS data increase. We applied the method to seven phenotypes in the UK Biobank, integrating expression data. We find height to be the most polygenic and asthma to be the least polygenic. [ABSTRACT FROM AUTHOR]

The relaxin-3/RXFP3 system has been implicated in the modulation of depressive- and anxiety-like behaviour in the animal literature; however, there is a lack of human studies investigating this signalling system. We seek to bridge this gap by leveraging the large UK Biobank study to retrospectively assess genetic risk variants linked with this neuropeptidergic system. Specifically, we conducted a candidate gene study in the UK Biobank to test for potential associations between a set of functional, candidate single nucleotide polymorphisms (SNPs) pertinent to relaxin-3 signalling, determined using in silico tools, and several outcomes, including depression, atypical depression, anxiety and metabolic syndrome. For each outcome, we used several rigorously defined phenotypes, culminating in subsample sizes ranging from 85,881 to 386,769 participants. Across all outcomes, there were no associations between any candidate SNP and any outcome phenotype, following corrections for multiple testing burden. Regression models comprising several SNPs per relevant candidate gene as exploratory variables further exhibited no prediction of outcome. Our findings corroborate conclusions from previous literature about the limitations of candidate gene approaches, even when based on firm biological hypotheses, in the domain of genetic research for neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

Osteoporosis, a complex chronic disease with increasing prevalence, is characterised by reduced bone mineral density (BMD) and increased fracture risk. The high heritability of BMD suggests substantial impact of the individual genetic disposition on bone phenotypes and the development of osteoporosis. In the past years, genome-wide association studies (GWAS) identified hundreds of genetic variants associated with BMD or osteoporosis. Here, we analysed 1103 single nucleotide polymorphisms (SNPs), previously identified as associated with estimated BMD (eBMD) in the UK Biobank. We assessed whether these SNPs are related to heel stiffness index obtained by quantitative ultrasound in 5665 adult participants of the Study of Health in Pomerania (SHIP). We confirmed 45 significant associations after correction for multiple testing. Next, we analysed six selected SNPs in 631 patients evaluated for osteoporosis [rs2707518 (CPED1/WNT16), rs3779381 (WNT16), rs115242848 (LOC101927709/EN1), rs10239787 (JAZF1), rs603424 (PKD2L1) and rs6968704 (JAZF1)]. Differences in minor allele frequencies (MAF) of rs2707518 and rs3779381 between SHIP participants (higher MAF) and patients evaluated for osteoporosis (lower MAF) indicated a protective effect of the minor allele on bone integrity. In contrast, differences in MAF of rs603424 indicated a harmful effect. Co-localisation analyses indicated that the rs603424 effect may be mediated via stearoyl-CoA desaturase (SCD) expression, an enzyme highly expressed in adipose tissue with a crucial role in lipogenesis. Taken together, our results support the role of the WNT16 pathway in the regulation of bone properties and indicate a novel causal role of SCD expression in adipose tissue on bone integrity. [ABSTRACT FROM AUTHOR]

Summary: Bone mineral density (BMD) is an essential predictor of osteoporosis and fracture. We conducted a genome-wide trajectory analysis of BMD and analyzed the BMD change. Purpose: This study aimed to identify the genetic architecture and potential biomarkers of BMD. Methods: Our analysis included 141,261 white participants from the UK Biobank with heel BMD phenotype data. We used a genome-wide trajectory analysis tool, TrajGWAS, to conduct a genome-wide association study (GWAS) of BMD. Then, we validated our findings in previously reported BMD genetic associations and performed replication analysis in the Asian participants. Finally, gene-set enrichment analysis (GSEA) of the identified candidate genes was conducted using the FUMA platform. Results: A total of 52 genes associated with BMD trajectory mean were identified, of which the top three significant genes were WNT16 (P = 1.31 × 10−126), FAM3C (P = 4.18 × 10−108), and CPED1 (P = 8.48 × 10−106). In addition, 114 genes associated with BMD within-subject variability were also identified, such as AC092079.1 (P = 2.72 × 10−13) and RGS7 (P = 4.72 × 10−10). The associations for these candidate genes were confirmed in the previous GWASs and replicated successfully in the Asian participants. GSEA results of BMD change identified multiple GO terms related to skeletal development, such as SKELETAL SYSTEM DEVELOPMENT (Padjusted = 2.45 × 10−3) and REGULATION OF OSSIFICATION (Padjusted = 2.45 × 10−3). KEGG enrichment analysis showed that these genes were mainly enriched in WNT SIGNALING PATHWAY. Conclusions: Our findings indicated that the CPED1-WNT16-FAM3C locus plays a significant role in BMD mean trajectories and identified several novel candidate genes contributing to BMD within-subject variability, facilitating the understanding of the genetic architecture of BMD. [ABSTRACT FROM AUTHOR]

Parkinson's disease has a large heritable component and genome-wide association studies have identified over 90 variants with disease-associated common variants, providing deeper insights into the disease biology. However, there have not been large-scale rare variant analyses for Parkinson's disease. To address this gap, we investigated the rare genetic component of Parkinson's disease at minor allele frequencies <1%, using whole genome and whole exome sequencing data from 7184 Parkinson's disease cases, 6701 proxy cases and 51 650 healthy controls from the Accelerating Medicines Partnership Parkinson's disease (AMP-PD) initiative, the National Institutes of Health, the UK Biobank and Genentech. We performed burden tests meta-analyses on small indels and single nucleotide protein-altering variants, prioritized based on their predicted functional impact. Our work identified several genes reaching exome-wide significance. Two of these genes, GBA1 and LRRK2 , have variants that have been previously implicated as risk factors for Parkinson's disease, with some variants in LRRK2 resulting in monogenic forms of the disease. We identify potential novel risk associations for variants in B3GNT3 , AUNIP , ADH5 , TUBA1B , OR1G1 , CAPN10 and TREML1 but were unable to replicate the observed associations across independent datasets. Of these, B3GNT3 and TREML1 could provide new evidence for the role of neuroinflammation in Parkinson's disease. To date, this is the largest analysis of rare genetic variants in Parkinson's disease. [ABSTRACT FROM AUTHOR]

Although glaucoma is a disease modulated by eye pressure, the mechanisms of pressure sensing in the eye are not well understood. Here, we investigated associations between mechanosensitive ion channel gene variants and primary open-angle glaucoma (POAG). Common (minor allele frequency > 5%) single nucleotide polymorphisms located within the genomic regions of 20 mechanosensitive ion channel genes in the K2P, TMEM63, PIEZO and TRP channel families were assessed using genotype data from the NEIGHBORHOOD consortium of 3853 cases and 33,480 controls. Rare (minor allele frequency < 1%) coding variants were assessed using exome array genotyping data for 2606 cases and 2606 controls. Association with POAG was analyzed using logistic regression adjusting for age and sex. Two rare PIEZO1 coding variants with protective effects were identified in the NEIGHBOR dataset: R1527H, (OR 0.17, P = 0.0018) and a variant that alters a canonical splice donor site, g.16-88737727-C-G Hg38 (OR 0.38, P = 0.02). Both variants showed similar effects in the UK Biobank and the R1527H also in the FinnGen database. Several common variants also reached study-specific thresholds for association in the NEIGHBORHOOD dataset. These results identify novel variants in several mechanosensitive channel genes that show associations with POAG, suggesting that these channels may be potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Chronic back pain (CBP) is a common debilitating condition with substantial societal impact. While understanding genotype-by-environment (GxE) interactions may be crucial to achieving the goals of personalized medicine, there are few large-scale studies investigating this topic for CBP. None of them systematically explore multiple CBP risk factors. To estimate the extent to which genetic effects on CBP are modified by known demographic and clinical risk factors. Case-control study, genome-wide GxE interaction study. Data on up to 331,610 unrelated participants (57,881 CBP cases and 273,729 controls) from the UK Biobank cohort were used. UK Biobank is a prospective cohort with collected deep genetic and phenotypic data on approximately 500,000 individuals across the UK. Self-reported chronic back pain. We applied a whole-genome approach to estimate the proportion of phenotypic variance explained by interactions between genotype and 12 known risk factors. We also analyzed if effects of common single-nucleotide polymorphisms on CBP are changed in presence of known risk factors. The results indicate a modest, if any, modification of genetic effects by examined risk factors in CBP. Our estimates suggest that detecting such weak effects would require a sample size of millions of individuals. The GxE interactions with examined common risk factors for CBP are either weak or absent. Interactions of such magnitude are unlikely to have the potential to inform and influence treatment strategies. Risk estimation models may use common genetic variation and the considered risk factors as independent predictors, without accounting for GxE. [ABSTRACT FROM AUTHOR]

Background: Recent studies had provided evidence that the gut microbiota is associated with sepsis. However, the potential causal relationship remained unclear Methods: The present study aimed to explore the causal effects between gut microbiota and sepsis by performing Mendelian randomization (MR) analysis utilizing publicly accessible genome-wide association study (GWAS) summarylevel data. Gut microbiota GWAS (N = 18,340) were obtained from the MiBioGen study and GWAS-summary-level data for sepsis were gained from the UK Biobank (sepsis, 10,154 cases; 452,764 controls). Two strategies were used to select genetic variants, i.e., single nucleotide polymorphisms (SNPs) below the locus-wide significance level (1 × 10-5) and the genome-wide statistical significance threshold (5 × 10-8) were chosen as instrumental variables (IVs). The inverse variance weighted (IVW) was used as the primary method for MR study, supplemented by a series of other methods. Additionally, a set of sensitivity analysis methods, including the MR-Egger intercept test, Mendelian randomized polymorphism residual and outlier (MR-PRESSO) test, Cochran's Q test, and leave-one-out test, were carried out to assess the robustness of our findings. Results: Our study suggested that increased abundance of Deltaproteobacteria, Desulfovibrionales, Catenibacterium, and Hungatella were negatively associated with sepsis risk, while Clostridiaceae1, Alloprevotella, LachnospiraceaeND3007group, and Terrisporobacter were positively correlated with the risk of sepsis. Sensitivity analysis revealed no evidence of heterogeneity and pleiotropy. Conclusion: This study firstly found suggestive evidence of beneficial or detrimental causal associations of gut microbiota on sepsis risk by applying MR approach, which may provide valuable insights into the pathogenesis of microbiota-mediated sepsis and strategies for sepsis prevention and treatment. [ABSTRACT FROM AUTHOR]

Genetic divergence is the fundamental process that drives evolution and ultimately speciation. Structural variants (SVs) are large-scale genomic differences within a species or population and can cause functionally important phenotypic differences. Characterizing SVs across invasive species will fill knowledge gaps regarding how patterns of genetic diversity and genetic architecture shape rapid adaptation under new selection regimes. Here, we seek to understand patterns in genetic diversity within the globally invasive European starling, Sturnus vulgaris. Using whole genome sequencing of eight native United Kingdom (UK), eight invasive North America (NA), and 33 invasive Australian (AU) starlings, we examine patterns in genome-wide SNPs and SVs between populations and within Australia. Our findings detail the landscape of standing genetic variation across recently diverged continental populations of this invasive avian. We demonstrate that patterns of genetic diversity estimated from SVs do not necessarily reflect relative patterns from SNP data, either when considering patterns of diversity along the length of the organism's chromosomes (owing to enrichment of SVs in subtelomeric repeat regions), or interpopulation diversity patterns (possibly a result of altered selection regimes or introduction history). Finally, we find that levels of balancing selection within the native range differ across SNP and SV of different classes and outlier classifications. Overall, our results demonstrate that the processes that shape allelic diversity within populations is complex and support the need for further investigation of SVs across a range of taxa to better understand correlations between often well-studied SNP diversity and that of SVs. [ABSTRACT FROM AUTHOR]

The rivers of southern England and northern France which drain into the English Channel contain several genetically unique groups of trout (Salmo trutta L.) that have suffered dramatic declines in numbers over the past 40 years. Knowledge of levels and patterns of genetic diversity is essential for effective management of these vulnerable populations. Using restriction site‐associated DNA sequencing (RADseq) data, we describe the development and characterisation of a panel of 95 single nucleotide polymorphism (SNP) loci for trout from this region and investigate their applicability and variability in both target (i.e., southern English) and non‐target trout populations from northern Britain and Ireland. In addition, we present three case studies which demonstrate the utility and resolution of these genetic markers at three levels of spatial separation:(a) between closely related populations in nearby rivers, (b) within a catchment and (c) when determining parentage and familial relationships between fish sampled from a single site, using both empirical and simulated data. The SNP loci will be useful for population genetic and assignment studies on brown trout within the UK and beyond. [ABSTRACT FROM AUTHOR]

Background: The bidirectional causal association between daytime napping frequency and schizophrenia is unclear. Methods: A bidirectional two-sample Mendelian randomization (MR) analysis was conducted with summary statistics of top genetic variants associated with daytime napping frequency and schizophrenia from genome-wide association studies (GWAS). The single nucleotide polymorphisms (SNPs) data of daytime napping frequency GWAS came from the UK Biobank (n = 452,633) and 23andMe study cohort (n = 541,333), while the schizophrenia GWAS came from the Psychiatric Genomics Consortium (PGC, 36,989 cases and 113,075 controls). The inverse variance weighted (IVW) analysis was the primary method, with the weighted median, MR-Robust Adjusted Profile Score (RAPS), Radial MR and MR-Pleiotropy Residual Sum Outlier (PRESSO) as sensitivity analysis. Results: The MR analysis showed a bidirectional causal relationship between more frequent daytime napping and the occurrence of schizophrenia, with the odds ratio (OR) for one-unit increase in napping category (never, sometimes, usually) on schizophrenia was 3.38 (95% confidence interval [CI]: 2.02–5.65, P = 3.58 × 10–6), and the beta for the occurrence of schizophrenia on daytime napping frequency was 0.0112 (95%CI: 0.0060–0.0163, P = 2.04 × 10–5). The sensitivity analysis obtained the same conclusions. Conclusion: Our findings support the bidirectional causal association between more daytime napping frequency and schizophrenia, implying that daytime napping frequency is a potential intervention for the progression and treatment of schizophrenia. [ABSTRACT FROM AUTHOR]

The causal effects of plasma lipid levels and the risk of retinal vascular occlusion (RVO) have not been clearly identified, especially for high-density lipoproteincholesterol (HDL-C) and low-density lipoprotein-cholesterol (LDL-C). Here, we try to identify these causal risk factors using a two-sample Mendelian randomization (MR) analysis. Single nucleotide polymorphisms (SNPs) were chosen as instrumental variables (IVs). We obtained genetic variants associated with lipid exposure at the genome-wide significance (P<5×10−8 ) level from a meta-analysis of GWAS from the Global Lipids Genetics Consortium (GLGC) based on 188,577 individuals of mostly European ancestry for MR analyses. Meanwhile, we used lipid GWAS from UK Biobank (UKB) with a sample size of 115,078 individuals as a supplement. We obtained genetic predictors of RVO from a FinnGen biobank study. We conducted both univariable and multivariable MR (MVMR) analyses to identify the causal effects of RVO. Although inverse variance weighted (IVW) was the primary method used for MR analyses, MR–Egger and weighted-median methods were used as supplements to IVW. We determined the heterogeneity of IVs using Cochrane’s Q test and I², and used the MR–Egger intercept and MR-PRESSO Global test to detect horizontal pleiotropy. A leave-one-out sensitivity analysis was conducted by removing a single variant from the analysis. Genetically predicted increased HDL-C level was associated with decreased risk of RVO from GLGC [OR=0.806; 95% CI=(0.659, 0.986); P=0.036], which was consistent with UKB results [OR=0.766; 95% CI=(0.635, 0.925); P=0.005]. MVMR analysis for plasma lipids [adjusted OR=0.639; 95% CI=(0.411, 0.992); P=0.046] or diabetes [adjusted OR=0.81; 95% CI=(0.67, 0.979); P=0.029] suggested that low HDL-C may be an independent risk factor for RVO. However, there was no evidence to support a causal association between LDL-C {GLGC [adjusted OR=1.015; 95% CI=(0.408, 2.523); P=0.975], UKB [OR=1.115; 95% CI=(0.884, 1.407); P=0.359]}, total cholesterol {GLGC [adjusted OR=0.904; 95% CI=(0.307, 2.659); P=0.854], UKB [OR=1.047; 95% CI=(0.816, 1.344); P=0.716]} or triglycerides {GLGC [OR=1.103; 95% CI=(0.883, 1.378); P=0.385], UKB [OR=1.003; 95% CI=(0.827, 1.217); P=0.098]} and RVO. Using two-sample MR analysis, our study suggested that dyslipidemia was a risk factor for RVO. Furthermore, our results indicated that a low HDL-C level may be an independent risk factor for RVO, suggesting that controlling HDL-C level may be effective in RVO development. [ABSTRACT FROM AUTHOR]

Brisk walkers are physically more active, taller, have reduced body fat and greater physical fitness and muscle strength. The aim of our study was to determine whether genetic variants associated with increased walking pace were overrepresented in elite sprinters compared to controls. A total of 70 single-nucleotide polymorphisms (SNPs) previously identified in a genome-wide association study (GWAS) of self-reported walking pace in 450,967 European individuals were explored in relation to sprinter status. Genotyping of 137 Russian elite sprinters and 126 controls was performed using microarray technology. Favorable (i.e., high-speed-walking) alleles of 15 SNPs (FHL2 rs55680124 C, SLC39A8 rs13107325 C, E2F3 rs4134943 T, ZNF568 rs1667369 A, GDF5 rs143384 G, PPARG rs2920503 T, AUTS2 rs10452738 A, IGSF3 rs699785 A, CCT3 rs11548200 T, CRTAC1 rs2439823 A, ADAM15 rs11264302 G, C6orf106 rs205262 A, AKAP6 rs12883788 C, CRTC1 rs11881338 A, NRXN3 rs8011870 G) were identified as having positive associations with sprinter status (p < 0.05), of which IGSF3 rs699785 survived correction for multiple testing (p = 0.00004) and was linked (p = 0.042) with increased proportions of fast-twitch muscle fibers of m. vastus lateralis in physically active men (n = 67). Polygenic analysis revealed that individuals with ≥18 favorable alleles of the 15 SNPs have an increased odds ratio of being an elite sprinter when compared to those with ≤17 alleles (OR: 7.89; p < 0.0001). Using UK Biobank data, we also established the association of 14 favorable alleles with low BMI and fat percentage, 8 alleles with increased handgrip strength, and 7 alleles with increased height and fat-free mass. In conclusion, we have identified 15 new genetic markers associated with sprinter status. [ABSTRACT FROM AUTHOR]

Background: Brain volume has been widely studied in the neuroimaging field, since it is an important and heritable trait associated with brain development, aging and various neurological and psychiatric disorders. Genome-wide association studies (GWAS) have successfully identified numerous associations between genetic variants such as single nucleotide polymorphisms and complex traits like brain volume. However, it is unclear how these genetic variations influence regional gene expression levels, which may subsequently lead to phenotypic changes. S-PrediXcan is a tissue-specific transcriptomic data analysis method that can be applied to bridge this gap. In this work, we perform an S-PrediXcan analysis on GWAS summary data from two large imaging genetics initiatives, the UK Biobank and Enhancing Neuroimaging Genetics through Meta Analysis, to identify tissue-specific transcriptomic effects on two closely related brain volume measures: total brain volume (TBV) and intracranial volume (ICV). Results: As a result of the analysis, we identified 10 genes that are highly associated with both TBV and ICV. Nine out of 10 genes were found to be associated with TBV in another study using a different gene-based association analysis. Moreover, most of our discovered genes were also found to be correlated with multiple cognitive and behavioral traits. Further analyses revealed the protein–protein interactions, associated molecular pathways and biological functions that offer insight into how these genes function and interact with others. Conclusions: These results confirm that S-PrediXcan can identify genes with tissue-specific transcriptomic effects on complex traits. The analysis also suggested novel genes whose expression levels are related to brain volumetric traits. This provides important insights into the genetic mechanisms of the human brain. [ABSTRACT FROM AUTHOR]

Bladder cancer, the most prevalent urological malignancy worldwide, accounts for a staggering 600,000 new cases annually. Its etiology is complex and multifactorial, influenced by various risk factors. Recent advances in genomic research, particularly genome-wide association studies (GWAS), have led to the discovery of new single nucleotide polymorphisms (SNPs) and associations in diverse cohorts. GWASs have proven to be a powerful tool for identifying links between genetic variants and SNPs. Additionally, phenome-wide association studies (PheWAS) have explored associations between specific genetic variants and a wide range of phenotypes, enabling the identification of genetic variants associated not only with bladder cancer but also with other diseases and traits that may share genetic risk factors. Our study, conducted using data from the UK Biobank's prospective cohort, aims to combine the findings from GWAS and PheWAS to identify both epidemiological and genetic risk factors associated with bladder cancer. 375,981 healthy individuals and 5,090 bladder cancer patients analyzed. Genotype calling, quality control, and imputation processes;previously described. Generic risk variants, like;single nucleotide polymorphisms (SNPs), selected based on conventional genome-wide significance thresholds (p<5×10-6) and specific criteria (MAF >0.01 and r 2 <0.001 for LD). Functional GWAS analysis using FUMA;performed to annotate GWAS results, prioritize genes,assess chromatic interaction mapping and tissue enrichment.To explore association between bladder cancer and other phenotypes, PheWas (phenome-wide association studies) associations conducted using GLM function in R. Covariates like sex, smoking, age, alcohol consumption, air pollution exposure, daily traffic intensity, dietary habits, and workplace exposure were included in all associations. Logistic regression models with log-transformed odds ratios;used for binary dependent variables. Statistical inferences relied on two-sided tests at significance level of 0.05, unless specified differently. The analysis done;using SAS software v.9.4 (SAS Institute, Cary, NC) and R v.3.6.0 software. Our study identified;SNPs;associated with increased bladdercancer risk, including variants in;PSCA;TMEM129;TERT;LYNX1-SLURP2;LSP1, and;HIPK1;(Fig1A). Protective associations for bladder cancer with SNPs within;UGTA1, THEM6, RAPGEF5, LY6K, LY6D, LNCOC1, JRK, and CLPTML;(Figure1A,B). Gene prioritization using FUMA and tissue mapping with chromatin interaction analysis revealed 9 lead SNPs across 8 genomic risk loci, with exonic SNPs exhibiting highest Combined Annotation Dependent Depletion;(CADD) scores indicating deleteriousness (Figure1C,D). Additionally, gene set enrichment analysis of common SNPs uncovered significant associations with biological processes like;Flavonoid & Xenobiotic Glucuronidation and Glucuronosyltransferase, closely linked to bladdercancer (Fig1E). PheWAS analysis revealed four phenotypes linked to bladder cancer risk, including prostate cancer (rs2242652), seborrheic keratosis and skin conditions (rs2242652), and BPH (rs31489) (Figure1F). Regression analysis for modifiable risk factors;demonstrated significant associations with bladder cancer susceptibility for BMI, heavy smoking, and male sex. Conversely, alcohol intake 3-4 drinks/week is protective. The study sheds light on various relevant SNPs and novel associations among germline mutations in bladder cancer, which have not been previously reported. Through further functional analysis, we discovered germline variants associated with glucuronosyltransferase activity, providing supporting evidence of toxin metabolism and its impact on bladder cancer risk. Additionally, our PheWAS analysis revealed co-associations with prostate cancer, prostatic hyperplasia, and dermatologic conditions linked to specific SNPs.Furthermore, our study reaffirmed well-known risk factors associated with bladder cancer, including sex and smoking, while also highlighting the protective effect of moderate alcohol consumption in the context of carcinogenesis.These findings have the potential to offer valuable insights into common pathways underlying bladder cancer, inform screening recommendations, and identify potential therapeutic targets for the disease. [ABSTRACT FROM AUTHOR]

Eosinophils play important roles in the release of cytokine mediators in response to inflammation. Many associations between common genetic variants and eosinophils have already been reported, using single nucleotide polymorphism (SNP) array data. Here, we have analyzed 200,000 whole-exome sequences (WES) from the UK Biobank cohort and performed gene-based analyses of eosinophil count. We defined five different variant weighting schemes to incorporate information on both deleteriousness and frequency. A total of 220 genes in 55 distinct (>10 Mb apart) genomic regions were found to be associated with eosinophil count, of which seven genes (ALOX15, CSF2RB, IL17RA, IL33, JAK2, S1PR4, and SH2B3) are driven by rare variants, independent of common variants identified in genome-wide association studies. Two additional genes, NPAT and RMI1, have not been associated with eosinophil count before and are considered novel eosinophil loci. These results increase our knowledge about the effect of rare variants on eosinophil count, which can be of great value for further identification of therapeutic targets. [ABSTRACT FROM AUTHOR]

Over the past decade, statistical methods have been developed to estimate single nucleotide polymorphism (SNP) heritability, which measures the proportion of phenotypic variance explained by all measured SNPs in the data. Estimates of SNP heritability measure the degree to which the available genetic variants influence phenotypes and improve our understanding of the genetic architecture of complex phenotypes. In this article, we review the recently developed and commonly used SNP heritability estimation methods for continuous and binary phenotypes from the perspective of model assumptions and parameter optimization. We primarily focus on their capacity to handle multiple phenotypes and longitudinal measurements, their ability for SNP heritability partition and their use of individual-level data versus summary statistics. State-of-the-art statistical methods that are scalable to the UK Biobank dataset are also elucidated in detail. [ABSTRACT FROM AUTHOR]

Background and aims: Various smoking behaviors, including smoking initiation, age of initiation, heaviness of smoking and smoking cessation, have been individually related to the risk of mortality; however, no study has assessed these smoking behaviors jointly in relation to mortality. Our study aimed to measure prospectively the association of panoramic smoking burden (PSB), generated from the four aforementioned smoking behaviors, with all‐cause and cause‐specific mortality, and measure whether such associations are modified by genetic variations. Design Prospective cohort study. Setting: UK Biobank. Participants: A total of 360 937 participants aged between 37 and 73 years were enrolled in 2006–10 and ‐followed‐up to 2018. Measurements The exposure was PSB, constructed based on four smoking behaviors including smoking initiation, age of initiation, heaviness of smoking and smoking cessation in a weighted method. A genetically determined PSB was also constructed with smoking‐associated single nucleotide polymorphisms (SNPs) and categorized into tertiles. The primary outcomes were all‐cause and cause‐specific mortality. Findings We identified 15 968 deaths [9022 from cancer and 5092 from cardiovascular disease (CVD)] over a median of 11.36 years' follow‐up. For all‐cause mortality, compared with participants with the PSB of zero, the hazard ratios of participants who had a PSB of one, two, three and four were 1.23 [95% confidence intervals (CI) = 1.18–1.29), 1.66 (95% CI = 1.59–1.75), 3.33 (95% CI = 3.17–3.51) and 5.76 (95% CI = 4.66–7.13), respectively. Among participants within each genetic risk category, low and intermediate PSB were associated with 45–58% reduced risk of all‐cause death compared with high PSB. Analysis of population‐attributable risk percentage indicated that 21.9, 19.1 and 24.7% of all‐cause‐, cancer‐ and CVD‐specific death could have been avoided if all ever smokers initiated smoking after age 18 years, smoked < 20 cigarettes/day and quit smoking. Conclusions: The panoramic smoking burden, based on smoking initiation, age of initiation, heaviness of smoking and smoking cessation, appears to be associated with all‐cause and cause‐specific mortality in a gradient manner with increasing panoramic smoking burden independent of other traditional and genetic risk factors. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies (GWASs) have consistently identified MAP2K5 as an obesity susceptibility gene. To deepen our understanding of the potential causal genetic variants of this region, a fine-mapping study of MAP2K5 was conducted. Methods and Results: SNPs rs7175517 (G > A) and rs4776970 (T > A) were identified as the leading SNPs associated with BMI in both Chinese and the United Kingdom populations. Second, colocalization of GWAS and expression quantitative trait loci (eQTL) analyses and bioinformatic analyses indicated that rs7175517 is the functionally leading variant in the MAP2K5 gene region. Dual-luciferase assays indicated that the G allele of rs7175517 reduced the mRNA expression of MAP2K5 in HEK293T cells. The possible mechanism was that the G allele interacted with more RNA repressors from nuclei extracts, which was evidenced by electrophoretic mobility shift assays (EMSAs). Furthermore, the pathway enrichment analyses of the products from DNA pull-down and protein mass spectrometry demonstrated that the G allele of rs7175517 might interact with RNA catabolic or splicing transcription factors, which consequentially increased adiposity deposition. Conclusion: SNP rs7175517 of the MAP2K5 gene was the putative causal variant associated with BMI. More precisely designed in vitro or animal experiments are warranted to further delineate the function of MAP2K5 in adipogenesis. [ABSTRACT FROM AUTHOR]

Background: Traditional observational studies have demonstrated an association between heart failure and Alzheimer's disease. The strengths of observational studies lie in their speed of implementation, cost, and applicability to rare diseases. However, observational studies have several limitations, such as uncontrollable confounders. Therefore, we employed Mendelian randomization of genetic variants to evaluate the causal relationships existing between AD and HF, which can avoid these limitations. Materials and Methods: A two-sample bidirectional MR analysis was employed. All datasets were results from the UK's Medical Research Council Integrative Epidemiology Unit genome-wide association study database, and we conducted a series of control steps to select the most suitable single-nucleotide polymorphisms for MR analysis, for which five primary methods are offered. We reversed the functions of exposure and outcomes to explore the causal direction of HF and AD. Sensitivity analysis was used to conduct several tests to avoid heterogeneity and pleiotropic bias in the MR results. Results: Our MR studies did not support a meaningful causal relationship between AD on HF (MR-Egger, p = 0.634 > 0.05; weighted median (WM), p = 0.337 > 0.05; inverse variance weighted (IVW), p = 0.471 > 0.05; simple mode, p = 0.454 > 0.05; weighted mode, p = 0.401 > 0.05). At the same time, we did not find a significant causal relationship between HF and AD with four of the methods (MR-Egger, p = 0.195 > 0.05; IVW, p = 0.0879 > 0.05; simple mode, p = 0.170 > 0.05; weighted mode, p = 0.110 > 0.05), but the WM method indicated a significant effect of HF on AD (p = 0.025 < 0.05). Because the statistical powers of IVW and MR-Egger are more than that of WM, we think that there is no causal effect of HF on AD. Sensitivity analysis and horizontal pleiotropy were not detected in the MR analysis. Conclusion: Our results did not provide significant evidence indicating any causal relationships between HF and AD in the European population. Therefore, more large-scale datasets or datasets related to similar factors are expected for further MR analysis. [ABSTRACT FROM AUTHOR]

To evaluate the influence AMD risk genomic variants have on macular thickness in the normal population. UK Biobank participants with no significant ocular history were included using the UK Biobank Resource (project 2112). Spectral-domain optical coherence tomography (SD-OCT) images were taken and segmented to define retinal layers. The influence of AMD risk single-nucleotide polymorphisms (SNP) on retinal layer thickness was analysed. AMD risk associated SNPs were strongly associated with outer-retinal layer thickness. The inner-segment outer segment (ISOS)-retinal pigment epithelium (RPE) thickness measurement, representing photoreceptor outer segments was most significantly associated with the cumulative polygenic risk score, composed of 33 AMD-associated variants, resulting in a decreased thickness (p = 1.37 × 10–67). Gene–gene interactions involving the NPLOC4-TSPAN10 SNP rs6565597 were associated with significant changes in outer retinal thickness. Thickness of outer retinal layers is highly associated with the presence of risk AMD SNPs. Specifically, the ISOS-RPE measurement. Changes to ISOS-RPE thickness are seen in clinically normal individuals with AMD risk SNPs suggesting structural changes occur at the macula prior to the onset of disease symptoms or overt clinical signs. [ABSTRACT FROM AUTHOR]

Background: Previous studies have suggested associations between testosterone, genetic factors, and a series of complex diseases, but the associations with the lifespan phenotype, such as health span, remain unclear. Methods: In this prospective cohort study, we analyzed 145,481 men and 147,733 women aged 38–73 years old from UK Biobank (UKB) to investigate the sex-specific associations of total testosterone (TT), free testosterone (FT), or polygenic risk score (PRS) with health span termination (HST) risk. At baseline, serum testosterone levels were measured. HST was defined by eight events strongly associated with longevity. PRS, an efficient tool combining the effect of common genetic variants to discriminate genetic risk of complex phenotypes, was constructed by 12 single-nucleotide polymorphisms related to health span from UKB (P ≤ 5.0 × 10−8). We used multivariable Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: With a median follow-up time of 7.70 years, 26,748 (18.39%) men and 18,963 (12.84%) women had HST. TT was negatively associated with HST in men [HR per standard deviation (SD) increment of log-TT: 0.92, 95% CI: 0.88–0.97]. Inversely, both TT (HR per SD increment of log-TT: 1.05, 95% CI: 1.02–1.08) and FT (HR per SD increment of log-FT: 1.08, 95% CI: 1.05–1.11) presented an increased risk of HST in women. PRS was positively associated with HST risk (quintile 5 versus quintile 1, men, HR: 1.19, 95% CI: 1.15–1.24; women, HR: 1.21, 95% CI: 1.16–1.27). Moreover, men with high TT and low genetic risk showed the lowest HST risk (HR: 0.80, 95% CI: 0.73–0.88), whereas HST risk for women with both high TT and genetic risk increased obviously (HR: 1.32, 95% CI: 1.19–1.46). Similar joint effects were observed for FT in both genders. Conclusions: We observed sex-specific associations that testosterone was negatively associated with HST risk in men and positively associated with HST risk in women. Genetic factors increased the HST risk, suggesting that participants with both high genetic risk and abnormal testosterone levels (high level in women or low level in men) should be the target for early intervention. Although our findings highlight the associations between testosterone and health span, further mechanistic studies and prospective trials are warranted to explore the causation behind. [ABSTRACT FROM AUTHOR]

Both clinical and genetic factors drive the risk of venous thromboembolism. However, whether clinically recorded risk factors and genetic variants can be combined into a clinically applicable predictive score remains unknown. Using Cox proportional-hazard models, we analyzed the association of risk factors with the likelihood of venous thromboembolism in U.K. Biobank, a large prospective cohort. We then created a polygenic risk score of 36 single nucleotide polymorphisms and a clinical score determined by age, sex, body mass index, previous cancer diagnosis, smoking status, and fracture in the last 5 years. Participants were at significantly increased risk of venous thromboembolism if they were at high clinical risk (subhazard ratio, 4.37 [95% CI, 3.85–4.97]) or high genetic risk (subhazard ratio, 3.02 [95% CI, 2.63–3.47]) relative to participants at low clinical or genetic risk, respectively. The combined model, consisting of clinical and genetic components, was significantly better than either the clinical or the genetic model alone (P < 0.001). Participants at high risk in the combined score had nearly an eightfold increased risk of venous thromboembolism relative to participants at low risk (subhazard ratio, 7.51 [95% CI, 6.28–8.98]). This risk score can be used to guide decisions regarding venous thromboembolism prophylaxis, although external validation is needed. [ABSTRACT FROM AUTHOR]

• Variants rs2269001 in KCNH2 is associated with paroxysmal tachycardia (PT). • Variants rs7789585 in KCNH2 is associated with atrial fibrillation/flutter (AF/AFL). • These two variants are correlated with physical measurements and neutrophil percentage. Cardiac arrhythmia, a common cardiovascular disease, is closely related to genetic polymorphisms. However, the associations between polymorphisms in KCNH2 and various arrhythmias remain inadequately explored. Guided by the assumption that KCNH2 genetic polymorphisms significantly contribute to the development of arrhythmias, we thoroughly explored the associations between 85 KCNH2 genetic variations and 16 cardiac arrhythmias in a sample obtained from the UK Biobank (UKBB, N = 307,473). The illnesses documented in the electronic medical records of the sample were mapped to a phecode system for a more accurate representation of distinct phenotypes. Survival analysis was used to test the effect of KCNH2 variants on arrhythmia incidence, and a phenotype-wide association study (PheWAS) was performed to investigate the effect of KCNH2 polymorphisms on 102 traits, including physical measurements, biomarkers, and hematological indicators. Novel associations of variants rs2269001 and rs7789585 in KCNH2 with paroxysmal tachycardia (PT) and atrial fibrillation/flutter (AF/AFL), respectively, were identified. Moreover, with an increase in the number of minor alleles of these two variants, the incidence rates of PT and AF/AFL decreased. In addition, the PheWAS results suggested that these two single nucleotide polymorphisms were associated with multiple parameters in physical measurements and neutrophil percentage. The multiple novel associations observed in this study illustrate the importance of KCNH2 genetic variations in the pathogenesis of arrhythmia. [ABSTRACT FROM AUTHOR]

Recent genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms affecting depressive disorders. GWAS results support the heterogeneity of depression as a disorder affected by a large number of genetic variants with mainly small effect sizes. However, not much is known about the interplay of different genetic risk factors. Moreover, recent studies are questioning the role of common candidate genes in the development of depressive disorders. One such candidate variant is the serotonin-transporter-promoter-polymorphism 5-HTTLPR in the SLC6A4 gene. We hypothesize that 5-HTTLPR exerts its effect on depressive disorders in interaction with other genetic variants. In the present study we test this hypothesis using a genome-wide gene-gene interaction approach on a large sample from the UK Biobank (N = 127,558). We identified a region in the DPF1 gene that displayed a genome-wide significant (p = 3.31 × 10−7) interaction effect with the biallelic version of 5-HTTLPR on lifetime depression. DPF1 has not previously been described as risk factor for depressive disorders but is exclusively expressed in the brain as a major regulator of neuronal development and neuroplasticity. This study stresses the need for further analyses that take into consideration the fact that genetic variants do operate in biological networks. • Biallelic 5-HTTLPR variant interacts with DPF1 gene on depression. • DPF1 new risk factor for depression; regulator of neurodevelopment, −plasticity. • Interaction analyses important in the quest to understand disease development. [ABSTRACT FROM AUTHOR]